GEOVISUAL ANALYTICS on the VERIFICATION of the PAGASA OPERATIONAL NUMERICAL WEATHER PREDICTION MODEL RAINFALL FORECAST

Abstract

Assessment of NWP model performance is an integral part of operational forecasting as well as in research and development. Understanding the bias propagation of an NWP model and how it propagates across space can provide more insight in determining underlying causes and weaknesses not easily determined in traditional methods. The study aims to introduce the integration of the spatial distribution of error in interpreting model verification results by assessing how well the operational numerical weather prediction system of PAGASA captures the country's weather pattern in each of its climate type. It also discusses improvements in model performance throughout the time-frame of analysis. Error propagation patterns were identified using Geovisual Analytics to allow comparison of verification scores among individual stations. The study concluded that a major update in the physics parameterization of the model in 2016 and continued minor updates in the following years, surface precipitation forecasts greatly improved from an average RMSE of 9.3, MAE of 3.2 and Bias of 1.36 in 2015 to an RMSE of 7.9, MAE of 2.5 and bias of-0.63 in 2018.